Will 2024 be the turning point of AI for banks and capital markets?
In financial services, AI is getting closer to a tipping point. A 2023 Ernst & Young survey found that leaders in banking and capital markets appear to be the least skeptical about the technology’s use cases, with banks like Citi and JP Morgan already using AI to improve customer experience and hide hidden unlock insights to inform trading decisions.
The generative AI revolution has accelerated this process due to advances in machine and deep learning, making it increasingly clear that 2024 has ushered in a new era for AI adoption by banks and capital markets. How will this likely turn out?
Director (Product Management), Digital Assets and Blockchain at Lab49.
Transforming the front office with generative AI
As AI has spread across almost all businesses, the question for banks is not ‘if’ AI will impact their operations, but how. For example, generative AI is expected to save banks a lot of time and effort, with a 2024 Accenture report highlighting how the predicted impact on the banking sector includes lower costs, faster revenue growth and more powerful contact center processes.
The real sweet spot, however, is in the front office. By 2023, Deloitte predicted that the world’s fourteen largest investment banks could increase front office productivity by 27% to 35% by using generative AI. This includes saving time on creating documents such as pitches, industry reports and due diligence. Through the ability to quickly consolidate information, generative AI can help reduce the cost of content creation, improve analytical capabilities, and improve electronification.
However, before organizations adopt generative AI at scale, they should carefully consider the associated risks to ensure their systems are protected and to increase stakeholder confidence in their approach. Companies will need to ensure that their current digital transformation strategies are advanced enough to both collaborate with each other and take full advantage of these new systems, or risk a time-consuming implementation process and the need for costly revisions.
AI-powered sales and trading
Today, banks are increasingly using AI as a ‘co-pilot’, allowing them to capture and synthesize customer information in a timely manner. A great example of this was the 2023 launch of an internal initiative at Morgan Stanley Wealth Management with OpenAI to provide more personalized customer insights. Several major tier 1 banks are also using AI to assist their commercial bankers in the products to offer each customer and in cross-selling efforts. One particular innovation is the ability of AI systems to recommend prices to bankers in real-time to secure the best possible deal price for a customer while effectively understanding the customer’s needs.
From a skills perspective, this will have far-reaching implications for reshaping the roles and responsibilities of bankers. PWC research from 2023 has already shown that employees are concerned about digital upskilling, with 39% of employees reporting that they currently do not receive sufficient training in digital and technology skills from their employer.
An extensive rollout of AI within banking organizations will also raise questions around the traditional apprenticeship model within IB and commercial banking, which currently relies on significant amounts of employee and analyst research. There are already signs that vacancy rates in the banking sector will decline significantly in 2024 and 2025, and LLMs will increasingly catch up where access to in-depth and high-quality fundamental analysis of companies, commodity markets and other sectors is required. Promotions to VP and Director level will increasingly focus on interpersonal skills, with customer contact becoming more valuable as modeling and product knowledge is taken over by AI models.
Financial services connections
Think of AI as a flashlight you hold in a dark room. The room is filled with information and actionable insights, hidden in the darkness that AI can help illuminate. As banks and financial institutions increasingly reorient their technology stacks and data infrastructure around generative AI and other variants, they will be able to feed LLMs large amounts of proprietary data, enabling connections and inferences that would not have been possible before. The use of AI in this way is already present outside of financial services. For example, the US space agency NASA is using AI-generated insights to design components that are lighter and stronger than any human could design.
A similar kind of insight discovery could be achieved within banks and market groups. By feeding AI models with holistic data sets consolidated from different systems and formats – including potentially synthetic data sets generated by generative AI – financial services companies may be able to discover insights that are not visible to the naked eye. This is even more true when attempting to model different scenarios for use in risk management or asset price forecasting, or to refine existing models based on certain conditions, such as macroeconomic indicators such as job numbers and inflation, or company-specific earnings reports. In the future, AI may even suggest rebalancing portfolios based on inputs in ways that are unexpected and effective.
In 2024, there is no doubt that the gap will continue to widen between the companies that embrace AI and the productivity gains it delivers, and those that do not. As we look ahead, we will see the generative AI offerings from companies like OpenAI and Microsoft increasingly combined with other forms of AI in financial services. As banks reap the potential benefits of the technology, AI will leave no department of financial services untouched. Ultimately, thinking about AI now will make a significant contribution to the financial sector this year and beyond.
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